"""
    SORT: A Simple, Online and Realtime Tracker
    Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
"""
from __future__ import print_function

import os
import shutil
import numpy as np

# import matplotlib
# matplotlib.use('TkAgg')
# import matplotlib.pyplot as plt
# import matplotlib.patches as patches
# from skimage import io

import time
import argparse
from filterpy.kalman import KalmanFilter

np.random.seed(0)


def linear_assignment(cost_matrix):
    try:
        import lap

        _, x, y = lap.lapjv(cost_matrix, extend_cost=True)
        return np.array([[y[i], i] for i in x if i >= 0])  #
    except ImportError:
        from scipy.optimize import linear_sum_assignment

        x, y = linear_sum_assignment(cost_matrix)
        return np.array(list(zip(x, y)))


def iou_batch(bb_test, bb_gt):
    """
    From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
    """
    bb_gt = np.expand_dims(bb_gt, 0)
    bb_test = np.expand_dims(bb_test, 1)

    xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
    yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
    xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
    yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
    w = np.maximum(0.0, xx2 - xx1)
    h = np.maximum(0.0, yy2 - yy1)
    wh = w * h
    o = wh / (
        (bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
        + (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1])
        - wh
    )
    return o


def convert_bbox_to_z(bbox):
    """
    Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
      [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
      the aspect ratio
    """
    w = bbox[2] - bbox[0]
    h = bbox[3] - bbox[1]
    x = bbox[0] + w / 2.0
    y = bbox[1] + h / 2.0
    s = w * h  # scale is just area
    r = w / float(h)
    return np.array([x, y, s, r]).reshape((4, 1))


def convert_x_to_bbox(x, score=None):
    """
    Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
      [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
    """
    w = np.sqrt(x[2] * x[3])
    h = x[2] / w
    if score is None:
        return np.array(
            [x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0]
        ).reshape((1, 4))
    else:
        return np.array(
            [x[0] - w / 2.0, x[1] - h / 2.0, x[0] + w / 2.0, x[1] + h / 2.0, score]
        ).reshape((1, 5))


class KalmanBoxTracker(object):
    """
    This class represents the internal state of individual tracked objects observed as bbox.
    """

    count = 0

    def __init__(self, bbox):
        """
        Initialises a tracker using initial bounding box.
        """
        # define constant velocity model
        self.kf = KalmanFilter(dim_x=7, dim_z=4)
        self.kf.F = np.array(
            [
                [1, 0, 0, 0, 1, 0, 0],
                [0, 1, 0, 0, 0, 1, 0],
                [0, 0, 1, 0, 0, 0, 1],
                [0, 0, 0, 1, 0, 0, 0],
                [0, 0, 0, 0, 1, 0, 0],
                [0, 0, 0, 0, 0, 1, 0],
                [0, 0, 0, 0, 0, 0, 1],
            ]
        )
        self.kf.H = np.array(
            [
                [1, 0, 0, 0, 0, 0, 0],
                [0, 1, 0, 0, 0, 0, 0],
                [0, 0, 1, 0, 0, 0, 0],
                [0, 0, 0, 1, 0, 0, 0],
            ]
        )

        self.kf.R[2:, 2:] *= 10.0
        self.kf.P[
            4:, 4:
        ] *= 1000.0  # give high uncertainty to the unobservable initial velocities
        self.kf.P *= 10.0
        self.kf.Q[-1, -1] *= 0.01
        self.kf.Q[4:, 4:] *= 0.01

        self.kf.x[:4] = convert_bbox_to_z(bbox)
        self.time_since_update = 0
        self.id = KalmanBoxTracker.count
        KalmanBoxTracker.count += 1
        self.history = []
        self.hits = 0
        self.hit_streak = 0
        self.age = 0

    def update(self, bbox):
        """
        Updates the state vector with observed bbox.
        """
        self.time_since_update = 0
        self.history = []
        self.hits += 1
        self.hit_streak += 1
        self.kf.update(convert_bbox_to_z(bbox))

    def predict(self):
        """
        Advances the state vector and returns the predicted bounding box estimate.
        """
        if (self.kf.x[6] + self.kf.x[2]) <= 0:
            self.kf.x[6] *= 0.0
        self.kf.predict()
        self.age += 1
        if self.time_since_update > 0:
            self.hit_streak = 0
        self.time_since_update += 1
        self.history.append(convert_x_to_bbox(self.kf.x))
        return self.history[-1]

    def get_state(self):
        """
        Returns the current bounding box estimate.
        """
        return convert_x_to_bbox(self.kf.x)


def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
    """
    Assigns detections to tracked object (both represented as bounding boxes)

    Returns 3 lists of matches, unmatched_detections and unmatched_trackers
    """
    if len(trackers) == 0:
        return (
            np.empty((0, 2), dtype=int),
            np.arange(len(detections)),
            np.empty((0, 5), dtype=int),
        )

    iou_matrix = iou_batch(detections, trackers)

    if min(iou_matrix.shape) > 0:
        a = (iou_matrix > iou_threshold).astype(np.int32)
        if a.sum(1).max() == 1 and a.sum(0).max() == 1:
            matched_indices = np.stack(np.where(a), axis=1)
        else:
            matched_indices = linear_assignment(-iou_matrix)
    else:
        matched_indices = np.empty(shape=(0, 2))

    unmatched_detections = []
    for d, det in enumerate(detections):
        if d not in matched_indices[:, 0]:
            unmatched_detections.append(d)
    unmatched_trackers = []
    for t, trk in enumerate(trackers):
        if t not in matched_indices[:, 1]:
            unmatched_trackers.append(t)

    # filter out matched with low IOU
    matches = []
    for m in matched_indices:
        if iou_matrix[m[0], m[1]] < iou_threshold:
            unmatched_detections.append(m[0])
            unmatched_trackers.append(m[1])
        else:
            matches.append(m.reshape(1, 2))
    if len(matches) == 0:
        matches = np.empty((0, 2), dtype=int)
    else:
        matches = np.concatenate(matches, axis=0)

    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


class Sort(object):
    def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
        """
        Sets key parameters for SORT
        """
        self.max_age = max_age
        self.min_hits = min_hits
        self.iou_threshold = iou_threshold
        self.trackers = []
        self.frame_count = 0

    def update(self, dets=np.empty((0, 5))):
        """
        Params:
          dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
        Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
        Returns the a similar array, where the last column is the object ID.

        NOTE: The number of objects returned may differ from the number of detections provided.
        """
        self.frame_count += 1
        # get predicted locations from existing trackers.
        trks = np.zeros((len(self.trackers), 5))
        to_del = []
        ret = []
        for t, trk in enumerate(trks):
            pos = self.trackers[t].predict()[0]
            trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
            if np.any(np.isnan(pos)):
                to_del.append(t)
        trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
        for t in reversed(to_del):
            self.trackers.pop(t)
        matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(
            dets, trks, self.iou_threshold
        )

        # update matched trackers with assigned detections
        for m in matched:
            self.trackers[m[1]].update(dets[m[0], :])

        # create and initialise new trackers for unmatched detections
        for i in unmatched_dets:
            trk = KalmanBoxTracker(dets[i, :])
            self.trackers.append(trk)
        i = len(self.trackers)
        for trk in reversed(self.trackers):
            d = trk.get_state()[0]
            if (trk.time_since_update < 1) and (
                trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits
            ):
                ret.append(
                    np.concatenate((d, [trk.id + 1])).reshape(1, -1)
                )  # +1 as MOT benchmark requires positive
            i -= 1
            # remove dead tracklet
            if trk.time_since_update > self.max_age:
                self.trackers.pop(i)
        if len(ret) > 0:
            return np.concatenate(ret)
        return np.empty((0, 5))


def order_det_res(root):
    files = os.listdir(root)
    for file in files:
        F = os.path.join(root, file)
        with open(F, "r") as f:
            lines = f.readlines()
        lines.sort(key=lambda x: int(x.split(",")[0]))
        with open(F, "w") as F:
            for line in lines:
                F.write(line)


def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description="SORT demo")
    parser.add_argument("--mode")  # TODO: what is mode
    # parser.add_argument('--save_path', type=str)
    parser.add_argument(
        "--display",
        dest="display",
        help="Display online tracker output (slow) [False]",
        action="store_true",
    )
    # parser.add_argument("--seq_path", help="Path to detections.", type=str, default='data')
    parser.add_argument(
        "--max_age",
        help="Maximum number of frames to keep alive a track without associated detections.",
        type=int,
        default=1,
    )
    parser.add_argument(
        "--min_hits",
        help="Minimum number of associated detections before track is initialised.",
        type=int,
        default=3,
    )
    parser.add_argument(
        "--iou_threshold", help="Minimum IOU for match.", type=float, default=0.3
    )
    parser.add_argument("--scene_idxes_file", type=str, help="File containing idxes of scenes to run tracking")
    parser.add_argument(
        "--from_agent", default=0, type=int, help="start from which agent"
    )
    parser.add_argument(
        "--to_agent", default=6, type=int, help="until which agent (index + 1)"
    )
    parser.add_argument(
        "--det_logs_path", default='', type=str, help="Det logs path (to get the tracking input)"
    )
    parser.add_argument("--split", type=str, help="[test/val]")
    args = parser.parse_args()
    return args


if __name__ == "__main__":
    args = parse_args()
    display = args.display
    scene_idxes_file = open(args.scene_idxes_file, "r")
    scene_idxes = [int(line.strip()) for line in scene_idxes_file]
    print(f'scenes to run: {scene_idxes}')

    for current_agent in range(args.from_agent, args.to_agent):
        total_time = 0.0
        total_frames = 0
        root = os.path.join(os.path.join(args.det_logs_path, f'{args.mode}/tracking{current_agent}'))
        order_det_res(root)
        det_results = os.listdir(root)
        save_path = f"./{args.mode}/agent{current_agent}"
        os.makedirs(save_path, exist_ok=True)
        for seq in det_results:
            mot_tracker = Sort(
                max_age=args.max_age,
                min_hits=args.min_hits,
                iou_threshold=args.iou_threshold,
            )  # create instance of the SORT tracker
            seq_dets = np.loadtxt(os.path.join(root, seq), delimiter=",")

            with open(os.path.join(save_path, seq.replace("det_", "")), "w") as out_file:
                if len(seq_dets) == 0:
                    continue

                print("Processing %s." % (os.path.join(root, seq)))
                for frame in range(int(seq_dets[:, 0].max())):
                    frame += 1  # detection and frame numbers begin at 1
                    dets = seq_dets[seq_dets[:, 0] == frame, 2:7]
                    dets[:, 2:4] += dets[:, 0:2]  # convert to [x1,y1,w,h] to [x1,y1,x2,y2]
                    total_frames += 1

                    start_time = time.time()
                    trackers = mot_tracker.update(dets)
                    cycle_time = time.time() - start_time
                    total_time += cycle_time

                    for d in trackers:
                        print(
                            "%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1"
                            % (frame, d[4], d[0], d[1], d[2] - d[0], d[3] - d[1]),
                            file=out_file,
                        )

        eval_dir = f"../TrackEval/data/trackers/mot_challenge/V2X-{args.split}{current_agent}/sort-{args.mode}/data"
        os.makedirs(eval_dir, exist_ok=True)
        for seq in scene_idxes:
            tracker_txt = os.path.join(save_path, f"{seq}.txt")
            if not os.path.exists(tracker_txt):
                continue

            shutil.copy(tracker_txt, os.path.join(eval_dir, f"{seq}.txt"))
        print(
            "Total Tracking took: %.3f seconds for %d frames or %.1f FPS"
            % (total_time, total_frames, total_frames / total_time)
        )
        print("Saved results to ", eval_dir)
